The large-scale virtualized Cloud data centers consume huge amount of electrical energy leading to high operational costs and emission of greenhouse gases. Virtual machine (VM) consolidation has been found to be a promising approach to improve resource utilization and reduce energy consumption of the data center. However, aggressive consolidation of VMs tends to increase the number of VM migrations and leads to over-utilization of hosts. This in turn affects the quality of service (QoS) of the applications running in the VMs. Thus, reduction in energy consumption and at the same time ensuring proper QoS to the Cloud users are one of the major challenges among the researchers. In this paper, we have proposed an energy efficient and QoS-aware VM consolidation technique in order to address this problem. We have used Markov chain-based prediction approach to identify the over-utilized and under-utilized hosts in the data center. We have also proposed an efficient VM selection and placement policy based on linear weighted sum approach to migrate the VMs from over-utilized and under-utilized hosts considering both energy and QoS. Extensive simulations using real-world traces and comparison with state-of-art strategies show that our VM consolidation approach substantially reduces energy consumption within a data center while delivering suitable QoS.

中文翻译：

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云数据中心中服务感知虚拟机整合的能量和质量

大规模虚拟化云数据中心消耗大量电能，导致高昂的运营成本和温室气体排放。已发现虚拟机 (VM) 整合是提高数据中心资源利用率和降低能源消耗的一种很有前景的方法。然而，VM 的积极整合往往会增加 VM 迁移的数量并导致主机的过度利用。这反过来又会影响 VM 中运行的应用程序的服务质量 (QoS)。因此，降低能耗并同时确保为云用户提供适当的 QoS 是研究人员面临的主要挑战之一。在本文中，我们提出了一种节能且具有 QoS 意识的 VM 整合技术来解决这个问题。我们使用基于马尔可夫链的预测方法来识别数据中心中过度使用和未充分利用的主机。我们还提出了一种基于线性加权求和方法的高效 VM 选择和放置策略，以在考虑能源和 QoS 的情况下从过度使用和未充分利用的主机迁移 VM。使用真实世界轨迹的广泛模拟以及与最先进策略的比较表明，我们的 VM 整合方法可显着降低数据中心内的能源消耗，同时提供合适的 QoS。